# Defining Categories and Series

### Defining Categories and Series

In the **Set Up** tab of the **Advanced Chart Builder**, your chart is built by selecting:

1. **A Category**: the dimension you want to group by (e.g., grade level, program, term)
2. **One or more Series**: the numeric fields you want to summarize and visualize (e.g., average GPA, total enrollment, number of logins)

Together, these selections define what’s shown in the chart preview on the right.

#### Choosing categories and series

* **Category**: Think of this as the “x-axis” for bar, column, or line charts, or the grouping label in charts like pie or donut.
  * Common examples: Department, Grade Level, Term, Student Status, Demographic Identifier
* **Series**: These are the numeric values summarized within each category, or what’s being measured or compared.
  * Common examples: Count of Students, Average Score, Total Credits Earned

{% hint style="success" %}
You can add multiple series to compare metrics side by side across your chosen category.
{% endhint %}

### Series Aggregation options

When you choose a series field, you can apply an aggregation to determine how the values are summarized. Available options include:

* **Sum**: Total of all values (e.g., total credits attempted by program)
* **Average (Mean)**: Average value (e.g., average GPA by term)
* **Min / Max**: Lowest or highest value in the group
* **Count**: Number of records (e.g., number of students in each grade level)
* **First / Last**: The first or last value in the group, based on row order

Use the aggregation dropdown in the **Set Up** tab to adjust these settings as needed.

### Swapping category and series

You can click “Switch Category and Series” in the Setup tab to flip how your data is visualized. This is especially useful when your chart doesn’t look quite right, or when a different framing tells a clearer story.

**Why it swapping the category and series may be helpful:**

* **Change the focus of the analysis**: Shift from looking at individual records or row-wise summaries to a columnar comparison of aggregated values.
* **Compare categories side-by-side**: Easily see how different categories perform against a common measure, eliminating the need to scroll through long lists.
* **Identify trends across different segments**: Observe patterns and variations across distinct groups of data.

**Common examples where swapping category and series may helpful:**

1. **Analyzing Student Performance Across Different Subjects**: An educator wants to quickly compare each student's average grade across different subjects, rather than seeing each individual grade entry. Depending on the data, swapping from a detailed view to a grouped view may show how a student is generally performing. 
2. **Comparing Enrollment in Different Course Types Over Time**: An administrator wants to see the total enrollment for "Required" versus "Elective" courses for each year, rather than a list of individual courses. Dependent on the data structure, a swap may result in the new view clearly showing the year-over-year trends for each course type.


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